RNAsnoop: efficient target prediction for H/ACA snoRNAs
- 1 Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria, 2 Bioinformatics Group, Department of Computer Science, 3 Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, 4 Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 5 RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlikstraße 1,D-04103 Leipzig, Germany and 6 The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, USA
- * To whom correspondence should be addressed.
- Received August 26, 2009.
- Revision received November 30, 2009.
- Accepted December 6, 2009.
Abstract
Motivation: Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many ‘orphan’ snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms.
Results: RNAsnoop implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based machine learning approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative information, it presents an efficient and reliable tool for the prediction of H/ACA snoRNA target sites. We apply RNAsnoop to identify the snoRNAs that are responsible for several of the remaining ‘orphan’ pseudouridine modifications in human rRNAs, and we assign a target to one of the five orphan H/ACA snoRNAs in Drosophila.
Availability: The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/∼htafer/RNAsnoop
Contact: htafer{at}tbi.univie.ac.at
Supplementary information: Supplementary data are available at Bioinformatics online.
- © The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
